Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Authors: Tiancheng Jin, Haipeng Luo
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is mostly theoretical, with no negative outcomes. |
| Researcher Affiliation | Academia | Tiancheng Jin University of Southern California EMAIL Haipeng Luo University of Southern California EMAIL |
| Pseudocode | Yes | Our final algorithm is shown in Algorithm 1. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any code or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiment setup details such as hyperparameters or training configurations. |